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Practical recipes and code for applying computational methods—text mining, topic modeling, network analysis, and spatial/archival techniques—to humanities resea
Presents a practical toolkit for researchers in the humanities to apply computational methods: corpus preparation, TF-IDF and topic modeling (LDA), social network analysis for correspondence, GIS/spatial humanities workflows, TEI archival encoding, and ethical considerations. The skill includes code snippets (Python) demonstrating preprocessing, TF-IDF, LDA topic modeling, and network construction.
Use when conducting digital humanities projects that require text analysis, topic discovery, mapping historical data, building correspondence networks, or preparing TEI-compliant digital editions. Useful for scholars transitioning from traditional qualitative work to reproducible quantitative methods.
Good fit for research-assistant agents and Python-capable toolchains (e.g., Codex/GPT code models, research automation agents).
This skill has not been reviewed by our automated audit pipeline yet.
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